Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into shufflechannel

paddle/fluid/operators/math/selected_rows_functor.cc

@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include <algorithm>
 #include <set>
 #include <unordered_map>
 
@@ -252,23 +253,26 @@ elementwise_add_to(const DeviceContext& ctx, BlasT<DeviceContext, T>* blas,
 template <typename T>
 struct MergeAdd<platform::CPUDeviceContext, T> {
   framework::SelectedRows operator()(const platform::CPUDeviceContext& context,
-                                     const framework::SelectedRows& input) {
+                                     const framework::SelectedRows& input,
+                                     const bool sorted_result = false) {
     framework::SelectedRows out;
-    (*this)(context, input, &out);
+    (*this)(context, input, &out, sorted_result);
     return out;
   }
 
   void operator()(const platform::CPUDeviceContext& context,
                   const framework::SelectedRows& input,
-                  framework::SelectedRows* output) {
+                  framework::SelectedRows* output,
+                  const bool sorted_result = false) {
     std::vector<const framework::SelectedRows*> inputs;
     inputs.push_back(&input);
-    (*this)(context, inputs, output);
+    (*this)(context, inputs, output, sorted_result);
   }
 
   void operator()(const platform::CPUDeviceContext& context,
                   const std::vector<const framework::SelectedRows*>& inputs,
-                  framework::SelectedRows* output) {
+                  framework::SelectedRows* output,
+                  const bool sorted_result = false) {
     if (inputs.size() == 0) {
       VLOG(3) << "no input! return";
       return;
@@ -301,6 +305,9 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
     }
     std::vector<int64_t> merge_rows(merged_row_set.begin(),
                                     merged_row_set.end());
+    if (sorted_result) {
+      std::sort(merge_rows.begin(), merge_rows.end());
+    }
     std::unordered_map<int64_t, size_t> rows_to_id;
     for (size_t i = 0; i < merge_rows.size(); ++i) {
       rows_to_id[merge_rows[i]] = i;

paddle/fluid/operators/math/selected_rows_functor.cu

@@ -266,7 +266,8 @@ __global__ void MergeAddKernel(const T* input, const int64_t* input_rows,
 template <typename T>
 struct MergeAdd<platform::CUDADeviceContext, T> {
   framework::SelectedRows operator()(const platform::CUDADeviceContext& context,
-                                     const framework::SelectedRows& input) {
+                                     const framework::SelectedRows& input,
+                                     const bool sorted_result = false) {
     framework::SelectedRows out;
     (*this)(context, input, &out);
     return out;
@@ -274,7 +275,8 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
 
   void operator()(const platform::CUDADeviceContext& context,
                   const framework::SelectedRows& input,
-                  framework::SelectedRows* output) {
+                  framework::SelectedRows* output,
+                  const bool sorted_result = false) {
     framework::Vector<int64_t> input_rows(input.rows());
     if (input_rows.size() == 0) {
       return;
@@ -312,7 +314,8 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
 
   void operator()(const platform::CUDADeviceContext& context,
                   const std::vector<const framework::SelectedRows*>& inputs,
-                  framework::SelectedRows* output) {
+                  framework::SelectedRows* output,
+                  const bool sorted_result = false) {
     if (inputs.size() == 0) {
       VLOG(3) << "no input! return";
       return;

paddle/fluid/operators/math/selected_rows_functor.h

@@ -81,13 +81,16 @@ struct MergeAdd {
   // unary functor, merge by adding duplicated rows in
   // the input SelectedRows object.
   framework::SelectedRows operator()(const DeviceContext& context,
-                                     const framework::SelectedRows& input);
+                                     const framework::SelectedRows& input,
+                                     const bool sorted_result = false);
   void operator()(const DeviceContext& context,
                   const framework::SelectedRows& input,
-                  framework::SelectedRows* output);
+                  framework::SelectedRows* output,
+                  const bool sorted_result = false);
   void operator()(const DeviceContext& context,
                   const std::vector<const framework::SelectedRows*>& inputs,
-                  framework::SelectedRows* output);
+                  framework::SelectedRows* output,
+                  const bool sorted_result = false);
 };
 
 enum class ScatterOps { ASSIGN, ADD, SUB, SUBBY, MUL, DIV, DIVBY };

paddle/fluid/operators/optimizers/adam_op.h

@@ -157,8 +157,11 @@ struct AdamFunctor<T, CPUAdam> {
   }
 };
 
+template <typename T, typename Flavour>
+struct SparseAdamFunctor;
+
 template <typename T>
-struct SparseAdamFunctor {
+struct SparseAdamFunctor<T, GPUAdam> {
   T beta1_;
   T beta2_;
   T epsilon_;
@@ -236,6 +239,106 @@ struct SparseAdamFunctor {
   }
 };
 
+template <typename T>
+struct SparseAdamFunctor<T, CPUAdam> {
+  T beta1_;
+  T beta2_;
+  T epsilon_;
+
+  const T* beta1_pow_;
+  const T* beta2_pow_;
+  const T* moment1_;
+  T* moment1_out_;
+  const T* moment2_;
+  T* moment2_out_;
+  const T* lr_;
+  const T* grad_;
+  const T* param_;
+  T* param_out_;
+
+  const int64_t* rows_;
+  int64_t row_numel_;
+  int64_t row_count_;
+
+  SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
+                    const T* beta2_pow, const T* mom1, T* mom1_out,
+                    const T* mom2, T* mom2_out, const T* lr, const T* grad,
+                    const T* param, T* param_out, const int64_t* rows,
+                    int64_t row_numel, int64_t row_count, bool lazy_mode)
+      : beta1_(beta1),
+        beta2_(beta2),
+        epsilon_(epsilon),
+        beta1_pow_(beta1_pow),
+        beta2_pow_(beta2_pow),
+        moment1_(mom1),
+        moment1_out_(mom1_out),
+        moment2_(mom2),
+        moment2_out_(mom2_out),
+        lr_(lr),
+        grad_(grad),
+        param_(param),
+        param_out_(param_out),
+        rows_(rows),
+        row_numel_(row_numel),
+        row_count_(row_count) {}
+
+  inline HOSTDEVICE void adam_update(size_t i, T g) const {
+    // The following code is the same as dense
+    T mom1 = moment1_[i];
+    T mom2 = moment2_[i];
+    T lr = *lr_;
+    T beta1_pow = *beta1_pow_;
+    T beta2_pow = *beta2_pow_;
+    T p = param_[i];
+
+    // Calculation
+    lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);
+
+    mom1 = beta1_ * mom1 + (1 - beta1_) * g;
+    mom2 = beta2_ * mom2 + (1 - beta2_) * g * g;
+    p -= lr * (mom1 / (sqrt(mom2) + epsilon_));
+
+    // Write back to global memory
+    moment1_out_[i] = mom1;
+    moment2_out_[i] = mom2;
+    param_out_[i] = p;
+  }
+
+  inline void operator()(size_t numel) const {
+    // lr could be reuse
+    T lr = *lr_;
+    T beta1_pow = *beta1_pow_;
+    T beta2_pow = *beta2_pow_;
+    lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);
+    size_t row_count = numel / row_numel_;
+
+    for (size_t i = 0U, j = 0U; i != row_count; ++i) {
+      if (i == *(rows_ + j)) {
+        for (size_t k = 0U; k != row_numel_; ++k) {
+          T g = grad_[j * row_numel_ + k];
+          adam_update(i * row_numel_ + k, g);
+        }
+        ++j;
+      } else {
+        for (size_t k = 0U; k != row_numel_; ++k) {
+          T mom1 = moment1_[i * row_numel_ + k];
+          T mom2 = moment2_[i * row_numel_ + k];
+          T p = param_[i * row_numel_ + k];
+
+          mom1 = beta1_ * mom1;
+          mom2 = beta2_ * mom2;
+
+          p -= lr * (mom1 / (sqrt(mom2) + epsilon_));
+          // Write back to global memory
+          moment1_out_[i * row_numel_ + k] = mom1;
+          moment2_out_[i * row_numel_ + k] = mom2;
+          param_out_[i * row_numel_ + k] = p;
+        }
+      }
+    }
+  }
+};
+
 template <typename DeviceContext, typename T>
 class AdamOpKernel : public framework::OpKernel<T> {
  public:
@@ -331,7 +434,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
                                    .Var()
                                    ->GetMutable<framework::SelectedRows>();
         merge_func(ctx.template device_context<DeviceContext>(), grad,
-                   grad_merge_var);
+                   grad_merge_var, true);
         grad_merge_ptr = grad_merge_var;
       }
 
@@ -347,32 +450,46 @@ class AdamOpKernel : public framework::OpKernel<T> {
       } else {
 #endif
         rows = grad_merge.rows().data();
-
 #if defined(PADDLE_WITH_CUDA)
       }
 #endif
       auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
 
-      SparseAdamFunctor<T> functor(
-          beta1, beta2, epsilon, beta1_pow.template data<T>(),
-          beta2_pow.template data<T>(), mom1.template data<T>(),
-          mom1_out.template mutable_data<T>(ctx.GetPlace()),
-          mom2.template data<T>(),
-          mom2_out.template mutable_data<T>(ctx.GetPlace()),
-          lr.template data<T>(), grad_data, param.template data<T>(),
-          param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
-          grad_merge.rows().size(), lazy_mode);
-      VLOG(3) << "lazy_mode :" << lazy_mode;
-      if (lazy_mode && platform::is_cpu_place(ctx.GetPlace())) {
-        size_t row_count = grad_merge.rows().size();
-        std::vector<int64_t> cpu_rows(grad_merge.rows());
-        for (size_t row_index = 0; row_index < row_count; ++row_index) {
-          for (size_t offset = 0; offset < row_numel; ++offset) {
-            size_t i = cpu_rows[row_index] * row_numel + offset;
-            functor.adam_update(i, grad_data[row_index * row_numel + offset]);
+      if (platform::is_cpu_place(ctx.GetPlace())) {
+        SparseAdamFunctor<T, CPUAdam> functor(
+            beta1, beta2, epsilon, beta1_pow.template data<T>(),
+            beta2_pow.template data<T>(), mom1.template data<T>(),
+            mom1_out.template mutable_data<T>(ctx.GetPlace()),
+            mom2.template data<T>(),
+            mom2_out.template mutable_data<T>(ctx.GetPlace()),
+            lr.template data<T>(), grad_data, param.template data<T>(),
+            param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
+            grad_merge.rows().size(), lazy_mode);
+
+        if (lazy_mode) {
+          size_t row_count = grad_merge.rows().size();
+          std::vector<int64_t> cpu_rows(grad_merge.rows());
+          for (size_t row_index = 0; row_index < row_count; ++row_index) {
+            for (size_t offset = 0; offset < row_numel; ++offset) {
+              size_t i = cpu_rows[row_index] * row_numel + offset;
+              functor.adam_update(i, grad_data[row_index * row_numel + offset]);
+            }
           }
+        } else {
+          functor(param.numel());
         }
-      } else {
+      } else if (platform::is_gpu_place(ctx.GetPlace())) {
+        SparseAdamFunctor<T, GPUAdam> functor(
+            beta1, beta2, epsilon, beta1_pow.template data<T>(),
+            beta2_pow.template data<T>(), mom1.template data<T>(),
+            mom1_out.template mutable_data<T>(ctx.GetPlace()),
+            mom2.template data<T>(),
+            mom2_out.template mutable_data<T>(ctx.GetPlace()),
+            lr.template data<T>(), grad_data, param.template data<T>(),
+            param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
+            grad_merge.rows().size(), lazy_mode);
+
+        // FIXME(minqiyang): remove BinarySearch in GPU later
         platform::ForRange<DeviceContext> for_range(
             static_cast<const DeviceContext&>(ctx.device_context()),
             param.numel());